Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central
nervous system (CNS), although the exact etiology and pathogenesis have not yet been
deciphered. The finding of IgG antibody formation specifically in the cerebrospinal
fluid (CSF), but not in a corresponding serum (i.e. oligoclonal banding), has long
been a useful test for diagnosis and differential diagnosis of MS [1], though no known
antigenic specificity has ever been universally defined. The search has been ongoing
for useful serum-derived biomarkers, including antibodies. Serum IgM antibodies to
an N-glucosylated peptide were specifically increased in relapsing-remitting
multiple sclerosis (RRMS) patients [2,3]. High antibody titers to two myelin
peptides, myelin oligodendrocyte glycoprotein and myelin basic protein were reported
by some [4], but not
others [5], to predict
early relapse in patients after their first presentation (FP) of MS.

We previously demonstrated elevated levels of IgM antibodies to
Glc(?1,4)Glc(?) (GAGA4) in RRMS patients in comparison to
patients with other neurological diseases (OND) [6]. We were, therefore, interested in
knowing when in the course of disease higher antibody titers to GAGA4 or a panel of
glucose-based glycans first occurs or whether there was any correlation to disease
activity by focusing on patients studied after their FP.

Materials and methods

Serum Samples

A retrospective study of frozen (-70?C) and rethawed serum samples
collected from patients at the time of diagnostic work-up for their FP were
later diagnosed as RRMS. The control group included sera samples taken from
patients with OND that were stored around the same time from routine samples
sent to the respective CSF diagnostic laboratories. Demographic and clinical
data were obtained from hospital records. Inclusion criteria for MS samples were
as follows: patient age (18-60 years) at time of sampling, follow-up for at
least 4 years from blood sampling, and diagnosis of RRMS according to Poser
criteria [7], or as
OND. Samples which meet the above criteria were identified from one of two serum
repositories located at the Ottawa Hospital-General Campus, Ottawa, Canada (Mark
S. Freedman) between the years 1993 and 2001 or the Cliniques Universitaires
Saint-Luc in Brussels, Belgium (Christian Sindic) between the years 1998 and
2002. Samples were collected under a broad consent for scientific research
allowing for multiple studies and approved by local ethics boards. Relapse was
defined as any new neurological event accompanied by symptoms or signs, or
significant worsening of previous symptoms or signs in the absence of fever that
lasted at least 48 hours. All samples were encoded at respective institutions
before being sent to Glycominds Ltd. laboratories for antibody
analysis?decoding occurred only after all the analyses were
completed. Three distinct cohorts were analyzed: cohort-A included 88 samples
(44 FP n = 44, OND n = 44), OND patients were
matched to the MS patients according to age and gender; cohort-B included 252
samples (FP n = 167, OND n = 85); and cohort-C
included 100 FP patients. All samples were assayed in a
?blinded? fashion.

Total IgM measurement

Total IgM level was measured as previously described [6] and reported in relative fluorescence
units (RFU) (cohort-A). For cohort-B and cohort-C, total IgM levels were
measured using a commercial enzyme-linked immunosorbent assay (ELISA) kit
(Bethyl laboratories, Montgomery, TX) according to manufacturer instructions and
reported in milligram per milliliter.

Enzyme-linked immunoassay of anti-GAGA4 IgM

In cohort-A and cohort-B, levels of anti-GAGA4 IgM were determined in IgG
depleted samples by enzyme-linked immunoassay (EIA) and normalized according to
the levels of total IgM as previously reported [6]. IgG depletion was performed using a
commercial mini Rapi-Sep? units (PanBio, Baltimore, MD)
according to manufacturer's instructions. p-nitrophenyl derivative of
GAGA4 were covalently attached to the surface of a 96-well microtiter plate via
a linker as previously described [8]. Serum samples were diluted 1:1200 in
a sample diluent (Cat. No. G300023, Glycominds, Lod, Israel), dispensed into the
wells in duplicates, and incubated for 120 min in 5?C, then washed
with wash buffer (Cat. No. G300022, Glycominds Ltd). Bound antibodies were
labeled with horseradish peroxidase-conjugated goat anti-human IgM type-specific
antibody (1:2000), washed, and 3, 3?, 5,
5?-tetramethylbenzidine was added for detection. After 30 min, the
enzymatic reaction was stopped with 1% sulfuric acid solution and optical
density (OD) of the wells was read at 450 nm with a Victor 1420 plate reader
(Wallac, Turku, Finland). In cohort-A, anti-GAGA4 OD levels were normalized by
dividing them by the square root of total IgM levels (RFU) multiplied by
106. Cutoff value was set as mean [OD/(total IgM ?
106)0.5] level of OND group + 2 standard deviations
(SD).

In cohort-B, in addition to the tested samples, each plate included positive- and
negative-control sera samples from MS patients and a calibrator sample
considered as 50 units. Anti-GAGA4 EIA units (EU) values were calculated for
each sample by dividing the sample ODs by the calibrator OD multiplied by 50.
Anti-GAGA4 EU were normalized for total IgM in serum samples, by dividing by the
square root of total IgM (mg/mL)0.5 corrected to gender by adding
0.17 (mg/mL)0.5 for male samples. Cutoff value for determination of
anti-GAGA4 positivity was 42 EU/(mg/mL)0.5. This cutoff was based on
receiver operator characteristic curve analysis for achieving 90% specificity
for RRMS. The coefficient of variation (CV) of anti-GAGA4 ELISA level was 11%
between wells in the same plate (intra-plate), and 15% for different assays
(inter-plate). Inter-plate constant variance for calibrator sample was 15%.

Levels of anti-GAGA2, -GAGA3, -GAGA4, and -GAGA6 IgM antibodies in cohort-C
samples were measured by immunofluorescence assay, using glass slides
patterned with teflon mask, creating 7 clusters of microwells with 32 wells
in each cluster (Figure
1A). An adhesive silicon superstructure (Figure 1B) was attached to the slide.
This silicon gasket defined wells for manual application of multiple serum
samples per slide. Each well was arrayed with glycan antigens and internal
controls (Figure
1C). p-nitrophenyl derivatives of GAGA2, GAGA3, GAGA4, and GAGA6
(Toronto Research Chemicals, Toronto, Canada) were covalently bound by a
linker to the glass slide as previously described [6].

Assay procedures

The slide wells were incubated for 60 min at room temperature with blocking
solution (400 ?L/well). After removal of blocking solution, 300
?L/well of patients? sera, diluted 1:40 in aqueous
solution of 1% bovine serum albumin in 20 mM Tris?HCl pH 7.2,
0.9% NaCl, 0.05% Tween-20 was added to each well. Each slide included five
sera samples and a reference sample for one arbitrary unit. Each sample was
tested five times on different slides. Samples were incubated for 45 min.
Following sera were removed and slides further washed and processed in an
HS4800 system. Briefly, slides were washed in TNTT buffer (20 mM
Tris?HCl pH 7.2, 2 M NaCl, 0.05% Tween-20, 0.05% Triton X-100) by
the hybridization system. Biotinylated goat antihuman IgM (1:500) and
Alexa-633-labeled streptavidin (1:150; Molecular Probes Inc. Eugene, OR,
USA) were incubated sequentially with washings in between for 1 h at
32?C in the light-protected and temperature-controlled
environment of the hybridization system.

Following the washing and drying, slides were scanned using laser scanner
(GenePix 4000B, Molecular Device, Baltimore, MD); slide image was analyzed
using Optiquant? software and RFU representing relative binding
of anti-glycan IgM to each antigen and control micro-well were calculated.
The data quality from each well was verified by ensuring signal levels from
human IgM and anti-human IgM spots above cutoff. If the data quality from
the wells did not meet criteria, samples were tested again. Levels of
anti-GAGA2, -GAGA3, -GAGA4, and -GAGA6 IgM were calculated for each sample
by dividing the sample RFU by the reference sample RFU. The CV of
anti-?-glucose levels was 8?12% for intra-slides wells
and 15?22% for different hybridization station running cycles
(inter-slide).

Samples were considered as positive if results were above cutoff levels for
at least one of the four antibodies. Cutoff values for each antigen were
calculated as mean value of the FP population plus 1, 1.5, or 2 SD, and best
fit cutoffs (4.0, 4.5, 4.5, and 4.3 for anti-GAGA2, -GAGA3, -GAGA4, and
-GAGA6 IgM, respectively).

Statistical methods

Numerical variables were compared across groups by Student's t-test or
by the Mann?Whitney U-test, depending on whether or not they followed
a normal distribution, and the ?2 test for rates
comparison between groups or the Fisher exact if any cells had an expected count
of less than 5. Pearson correlations were calculated between numerical
variables. P-values of less than 0.05 were considered to be statistically
significant. Uncertainty of results was expressed by 95% confidence intervals.
For comparison between FP patients and OND patients, levels of anti-GAGA4 IgM EU
were corrected for total IgM level by dividing them by the square root of total
human IgM in the sample. Diagnostic accuracy was calculated by sensitivity,
specificity, positive predictive value (PPV), and negative predictive value
(NPV). Predictive values were calculated based on the prevalence of MS in the
present cohort (0.68). The cumulative risk of the development of clinically
definite MS (CDMS) was calculated for each group according to the
Kaplan?Meier method, and the differences between the groups were
evaluated in a univariate analysis by the log-rank test.

We implemented a two-stage analysis for testing the ability of GAGA4 to
differentiate OND from FP subjects. In the first stage, a preliminary analysis
on OND and FP groups that were age- and gender-matched (cohort-A) were
performed. Our aim was to explore the ability of an anti-GAGA4 assay to
differentiate between OND and FP subjects under conditions that maximize the
power of detection of disease-specific effects. In the second stage, we tested
if the results from the preliminary analysis could be repeated by a more optimal
EIA method in a different and relatively larger population in which the FP and
OND groups were not matched for age and gender (cohort-B).

For testing the ability of the marker antibodies to differentiate FP patients
with a high risk vs. a low risk for early conversion to CDMS (cohort-C), several
cutoff values were investigated. Although this analysis involved the application
of multiple statistical tests that would normally require the application of a
correction factor, our study was in fact exploratory, which beckons multiple
analyses in order to obtain testable hypotheses that would require confirmation
in subsequent studies [9]. We nevertheless applied a Bonferroni correction factor to our
analysis.

Results

Cohort-A: Anti-GAGA4 IgM in FP-RRMS vs. OND group

Clinical and demographic characteristics as well as anti-GAGA4 levels for
cohort-A are described in Table 1. The OND and FP groups were matched for age and gender. In
cohort-A, there were significant differences between the Brussels and Ottawa
groups regarding gender composition, and a higher number of samples from Ottawa
in the FP group versus the OND group. However, no significant difference
regarding the levels of total IgM and anti-GAGA4 IgM normalized to total IgM was
found between the 22 samples from Brussels in comparison to the 66 samples from
Ottawa. Levels of anti-GAGA4 IgM normalized to total IgM were significantly
higher (P = 0.01) in samples from FP patients compared to OND controls.
Distribution of normalized anti-GAGA4 IgM in the FP and OND groups is shown in
Figure 2.

Using the results from the OND patients, we set a cutoff value for anti-GAGA4 IgM
antibodies of mean OD + 2 SD as 0.53[OD/(Total IgM RFU ?
106)0.5]. Using this cutoff value it was possible to
identify patients who had high levels of anti-GAGA4, who were later diagnosed as
RRMS with a sensitivity of 27.3% (95% -CI [15?43]) and specificity of
97.7% (95% CI [88?99]), PPV 92.3%, and NPV 52.3%.

Cohort-A: Identifying FP patients who will have a second attack within 24
months

Data regarding the time period between blood extraction and first relapse was
available for 41/44 FP patients. Twenty-six out of forty-one FP patients (63%)
had their first relapse within 2 years of blood sampling. Mean age of these
patients was significantly lower than those suffering from a second attack later
(mean age 34 versus 42 years, P = 0.001); however, no association was found
between age and anti-GAGA4 IgM levels among the study population. To examine for
a relationship between anti-GAGA4 IgM levels and the risk of an earlier (i.e. 2
vs. 4 years) second attack, we looked at patients whose anti-GAGA4 IgM levels
were above the median antibody levels for the FP group as a whole. Sixteen out
of twenty patients (80%) with antibody titers above median had a second clinical
attack within 2 years compared to only 10/21 (47%) patients with titers equal or
below the median (odds ratio 4.4 CI 95% 1.1?17.7, Fisher exact test,
P = 0.05).

Cohort-B: Anti-GAGA4 IgM in FP-RRMS versus OND group

Clinical and demographic characteristics as well as anti-GAGA4 levels for
cohort-B are described in Table 2. Total IgM levels were significantly higher in the FP group,
and associated with gender and anti-GAGA4 IgM levels, therefore, we corrected
the total IgM for gender by adding 0.17 (mg/mL)0.5 to all male
samples. Anti-GAGA4 EU were normalized for total IgM in serum samples by
dividing by the square root of total IgM (mg/mL)0.5 corrected to
gender. There were significant differences between the OND group versus the FP
group in mean age, gender composition, total IgM corrected for gender, and
anti-GAGA4 IgM. However, more importantly, the levels of anti-GAGA4 corrected
for total IgM was observed to be significantly higher (P = 0.0001,
Mann?Whitney U-test) in FP patients as opposed to OND patients.
Distribution of normalized anti-GAGA4 IgM in the FP and OND groups is shown in
Figure 3.

Using a cutoff of 42 anti-GAGA4 IgM (EU)/square root total IgM (mg/mL serum), we
found that 44/167 (26.3%) FP patients were positive, whereas 77/85 (90.6%) OND
patients were negative, corresponding to a sensitivity of 26.3% (95% CI
[19.8?33.7]), a specificity of 90.6% (95% CI [82.3?95.4]),
PPV of 84.6%, and NPV of 38.5%. Inter-plate CV for calibrator sample was
15%.

Cohort-C: Levels of anti-? glucose IgM in FP patients that will
have a second early relapse (up to 24 months), versus late or no relapse

Except for time to first relapse, no significant differences were found among
demographics, clinical characteristics, and square root total IgM of the early
versus late relapsing group. Therefore, in this cohort, there was no need to
correct for total IgM. Levels of all anti-glycan antibodies were higher in the
early versus later relapsers, however, this did not reach statistical
significance. To evaluate the possible relationship between levels of
anti-?-glucose-based glycans IgM levels and the risk of imminent
(i.e. within 24 months) first relapse, we looked at positive patients whose
antibodies levels were above cutoff levels for at least one of the four
antibodies versus patients negative for all four antibodies. Twenty-two (22/58
(38%) early relapsing patients were positive for at least one antibody compared
to only 5/42 (12%) positive patients that had a late or no relapse at all, (P =
0.003 ?2 test, odds ratio = 4.5 (95% CI
[1.5?13.2]), 0.0125 (Bonferroni correction) should be considered the
significant threshold for this analysis since four different methods for
determining the cutoff values of the antibodies was applied. In the group of
patients who did not experience a second attack within the study period, only
one was antibody-positive. Kaplan? Meier survival plot comparison
(Figure 4) between
cumulative risk of FP patients who were positive for at least one marker
(anti-GAGA4, -GAGA2, -GAGA3, or -GAGA6 IgM) versus negative patients revealed
significant differences between the groups (P = 0.0025 for up to 24 months,
respectively) indicating that antibody positive patients consistently had their
first relapse earlier. A high level in at least one of the
anti-?-glucose IgM antibodies, identified 37.9% of FP patients who
had an early attack (<24 months) versus those who had a late or no
attack within the follow-up period with 88.1% specificity, 81.5% PPV, and 50.7%
NPV (Table 4).

Discussion

A diagnostic or prognostic biomarker involving a simple serological test would
represent a significant advance in the management of relapsing MS. There are
numerous candidate serum antibodies that are purported to be useful as MS
biomarkers. Of these, it is notable that a number of anti-glycan antibodies have
been considered [2,3,10]. As a result of systematic screening
using the GlycoChip? glycan array, we previously found
significantly elevated levels of anti-GAGA4 IgM antibodies in MS patients compared
to OND patients. These levels differentiated MS from OND with 57% sensitivity and
85% specificity [6].

The current work extends these observations by examining how early in the course of
disease the antibodies can be found and determining whether their titers are
predictive of disease activity.

Testing a total of 311 frozen sera samples taken from FP patients at or near the time
of their first neurological event, and 129 samples from OND patients, we showed that
MS patients express higher levels of anti-GAGA4 IgM antibodies, indicating that
anti-glycan reactivity probably occurs very early in the disease course. More
importantly, we were able to demonstrate that FP patients with higher levels of a
panel of anti-?-glucose IgM antibodies had a higher probability for
having a second attack within 24 months. In fact, patients who are positive for at
least one of the anti-?-glucose IgM antibodies had significantly higher
cumulative risk (Kaplan?Meier) for having an earlier relapse, identifying
about a third of all the early relapsers. An interesting categorical analysis
revealed that anti-GAGA6 status alone (positive or negative) had the strongest
predictive value of an early relapse.

In the initial discovery phase [6] we screened 40 different glycans and found that IgM (not IgG or IgA)
antibodies to ?-glucose antigens could distinguish MS patients from OND
controls. This type of IgM response to carbohydrates is most likely produced by
self-replenishing B-1 B cells, which respond poorly to protein, but much better to
carbohydrate antigens [11]. In general, B1 B-cells require a high amount of antigen for induction
and play an important role as a first line of defense against invading pathogens,
removal of senescent cells, cell debris, and other self-antigens [12,13]. Interestingly, serum-derived human IgM
monoclonal antibodies were found to accumulate in areas of CNS damage and promote
remyelination in demyelinated mice [14,15]. This possibly reflects a type of
?house-keeping? role, a recognized property of these
antibodies [12]. Also, in
agreement with the present study, higher levels of IgM antibodies in CSF were found
to predict a more severe MS course [16]. It would, therefore, be worthwhile to
also test for the presence of anti-?-glucose IgM antibodies in the CSF of
MS patients; although, a simple blood test would still be more preferable,
especially if it needed to be repeated.

The biological basis of a humoral response to ?-glucose antigen is still
unclear, but it is of interest that this particular carbohydrate
(?-glucose) is found within the type IV collagen matrix of the
blood?brain barrier (BBB) [17]. A common structural feature of type IV
collagen is hydroxyllysine-linked disaccharides that are comprised of
?-glucose and ?-galactose subunits
(Glc(?1,2)Gal(?)) [18]. This disaccharide was previously
reported to be the most important immunogenic antigen of patients having
anti-glomerular basement membrane antibody?mediated glomerulonephritis
[19]. As immune cells
traverse the BBB, facilitated by the release of metalloproteases that help to
further breakdown the BBB extracellular matrix, the ongoing inflammatory response
can lead to the release of this carbohydrate antigen, thus stimulating an immune
reaction. The subsequent development of IgM antibodies could then also contribute to
further damage to the BBB by attacking the antigen in situ. Interestingly, type IV
collagen deposits can also be found within MS plaques [20]. Also, similar to the present study, an
IgM antibody response to glycans released from degrading collagen was reported for
rheumatoid arthritis [21], where authors claimed that the natural IgM antibodies were produced
against the glycosaminoglycans, including glucose molecules, which were degraded and
released from the cartilage matrix. In addition,
?-glucose?based polysaccharides are found in the cell wall of
several pathogenic fungi [22] and bacteria [23,24]. This
homology between known pathogens and a human ?-glucose glycan may suggest
that IgM antibodies could arise through the mechanism of molecular mimicry,
triggering a cross-reactive response that targets the same glycan in the BBB [25].

As this was a retrospective study, we did not uniformly perform timely MRI studies as
is the case today for making a definitive diagnosis based on the new McDonald
criteria [26,27]. It might have been of
interest to compare early MRI parameters predictive of disease to antibody levels to
formulate an overall prediction of an early first relapse (i.e. CDMS). Analysis of
the Early Treatment of MS (ETOMS) study population (308 FP patients and an abnormal
MRI) showed that out of the 121 patients who converted to CDMS within 2 years of
follow-up, 112 (93%) had ?3 modified Barkhof MRI criteria [28]. However, 155 out of
the 187 patients (83%) who didn't convert to CDMS within 24 months also
had ?3 modified Barkhof MRI criteria [26?30], yielding a 93% sensitivity, but only
17% specificity, PPV 42%, and NPV 78% for MRI predicting early CDMS conversion. MRI
seems more sensitive at predicting ultimate vs. imminent conversion to CDMS.
Although this cannot be directly compared to the diagnostic performance of antibody
measurement in our retrospective study of FP patients (37% sensitivity, 88%
specificity, 81% PPV, and 50% NPV), it suggests that measuring
anti-?-glucose IgM levels could provide an independent and more specific
predictive factor for early conversion to CDMS (within 24 months). Finding higher
levels of serum anti-GAGA2, -GAGA3, -GAGA4, or -GAGA6 IgM antibodies in FP patients
seems to predict, with high specificity, those who will convert to CDMS within 2
years. Such information might be invaluable for physicians having some difficulty in
deciding upon a course of early therapy for their FP patients. Definitive
conclusions can only be drawn, however, from a prospective study on additional
cohorts that measures anti-glycan antibodies together with MRI and clinical
outcomes.

Acknowledgments

Nir Dotan and Avinoam Dukler are employees and share holders in Glycominds Ltd., and
have filed a patent regarding the use of anti-GAGA2, -GAGA3, -GAGA4, and -GAGA6, for
diagnosis and prognosis of MS. Rom T. Altstock was a Glycominds Ltd. employee from
July 2001 to September 2007. MSF received funds from Glycominds to offset the cost
of preparing, coding and shipping samples for this study. The authors would like to
acknowledge Dr. Jennifer Yarden for her assistance in preparation and reviewing of
the manuscript, as well as for contributing the hypothesis regarding the source of
anti-?-glucose antigens.

Schwarz M,Spector L,Gargir A,et al. A new kind of carbohydrate array, its use for
profiling antiglycan antibodies, and the discovery of a novel human
cellulose-binding antibody. GlycobiologyYear: 2003;13:749?75412851287

Diagnostic characteristics for identification of FP patients who had an
early relapse (?24 months, n = 58) vs. late
or no relapse (?24 months, n = 42) using a
panel of anti-?-glucose disaccharide and different cutoffs
(Cohort C)